3D Track Assessment Method

ABSTRACT

A 3D track assessment method is disclosed for identifying and assessing features of a railway track bed based on 3D elevation and intensity data gathered from the railway track bed.

CROSS-REFERENCE(S) TO RELATED APPLICATION(S)

This application is a continuation application claiming priority to U.S.Nonprovisional patent application Ser. No. 14/725,490 entitled “3D TRACKASSESSMENT SYSTEM AND METHOD” which was filed on May 29, 2015 whichclaims priority to U.S. Provisional Patent Application Ser. No.62/118,600 entitled “3D Track Assessment System Post-Processing,Analysis and Reporting System” which was filed on Feb. 20, 2015, theentireties of which are incorporated herein by reference.

FIELD

This disclosure relates to the field of railway track inspection andassessment systems.

BACKGROUND

Rail infrastructure owners are motivated to replace the time consumingand subjective process of manual crosstie (track) inspection withobjective and automated processes. The motivation is driven by a desireto improve rail safety in a climate of increasing annual rail trafficvolumes and increasing regulatory reporting requirements. Objective,repeatable, and accurate track inventory and condition assessment alsoprovide owners with the innovative capability of implementingcomprehensive asset management systems which includeowner/region/environment specific track component deterioration models.Such rail specific asset management systems would yield significanteconomic benefits in the operation, maintenance and capital planning ofrail networks.

A primary goal of such automated systems is the non-destructivehigh-speed assessment of railway track infrastructure. Track inspectionand assessment systems currently exist including, for example,Georgetown Rail (GREX) Aurora 3D surface profile system and Ensco Rail2D video automated track inspection systems. Such systems typically usecoherent light emitting technology, such as laser radiation, toilluminate regions of the railway track bed during assessmentoperations.

An important consideration after field data collection of railway datais the manner in which the data is processed. One of the mosttime-consuming tasks is to identify different railway track features andto categorize and track such railway track features.

What is needed, therefore, is a robust and reliable system for analyzingand processing data collected during and/or after a high speedassessment of a railway track. What is also needed is a system that isable to quickly and accurately identify railway track features andassociate measured parametric data with those features.

SUMMARY

The above and other needs are met by a three dimensional trackassessment system (“3DTAS”). The 3DTAS has a number of novel featuresincluding surface elevation model 3D block matching based correlation;extraction, identification, and categorization of unfamiliar 3D trackfeatures; detection of rail head and rail base locations; detection andcategorization of railway tie distresses; measuring and reporting ofballast level anomalies (leading/trailing berms/voids indicating railstress, shoulder ballast voids); reporting the location and type of tieanchors (and the offset from the corresponding tie edge); measuring andreporting the location, size and type of rail joint bars (and detect andreport the presence of the through bolts and nuts); reporting thepresence of rail base welds (and any planar vertical deviations acrossthe weld due to differences in rail height, and the distance of the weldfrom the nearest tie); measuring and reporting the presence and severityof rail head distortion (crushed heads or corrugation) includingbattered joints; and the reporting and identification of types of othertrack materials (OTM).

The 3DTAS algorithms run on a system processor as described herein whichautomatically processes full width track surface elevation and intensitydata to identify 3D features and extract physical parameters ofinterest. Such discrete 3D feature identification and analysis methodsare based on surface elevation model (3D) block matching basedcorrelation. As unfamiliar features are encountered, 3D surface modelsfor the features are developed and physical parameters are defined forextraction. The extensibility of the rule-based expert systemarchitecture used for interpretation during processing allows therefinement of existing parameters and/or the development of rules andphysical parameters as new features or track components are encountered.

In one embodiment, tie condition (distress) is detected and categorizedbased on acquired 3D data. Condition analysis algorithms define theseverity (based on depth, width and/or proximity to other features suchas spikes or tie-ends for example) and extent (based on the area or theend to end length of the distress for example) of all surfacedistresses. These individual distresses are combined using developedclient specific algorithms to rate the quality of each tie. Eachdistress feature is recorded and maintained in a fully referencedfeature database that allows future comparisons at the individualdistress level. The objective, accurate and repeatable measurementspossible with the 3DTAS system allows the direct comparison ofindividual distresses and distress components on a tie-by-tie basis forsubsequent surveys (temporal comparison), an important capability forthe development of accurate deterioration models required for assetmanagement system development.

A system for assessing a railway track bed is disclosed, the systemcomprising a power source; a light emitting apparatus powered by thepower source for emitting light energy toward a railway track; a datastorage apparatus in communication with at least one processor; at leastone sensor for sensing reflected light that was emitted from the lightemitting apparatus and acquiring three dimensional image data of therailway track to be stored in the data storage apparatus, wherein theplurality of sensors are in communication with the at least oneprocessor; and the at least one processor wherein the at least oneprocessor is configured to run an algorithm for processingthree-dimensional elevation data gathered from the plurality of sensorsand saved in the data storage apparatus, the algorithm comprising thesteps of: (a) acquiring three dimensional data representative of asegment of railway track bed; (b) generating a track elevation map basedon the acquired three dimensional data; (c) identifying a railway trackbed feature from the track elevation map; and (d) storing informationcorresponding to the identified railway track bed feature in the datastorage apparatus.

The algorithm step of identifying a railway track bed feature mayfurther include the step of identifying a rail head edge by detectingsignificant vertical gradient edges over a two dimensional area whereinsuch vertical gradient edges are greater than a minimum rail heightthreshold.

The algorithm step of identifying a railway track bed feature mayfurther include the step of identifying a rail base edge by detectingsignificant vertical gradient edges over a two dimensional area adjacentthe detected rail head edge wherein such vertical gradient edges aregreater than a minimum rail base height threshold.

The algorithm described above may further include the step of removingdata corresponding to the rail head from the elevation map, therebyenhancing the detection of other smaller vertical components of therailway track bed.

The algorithm step of identifying a railway track bed feature mayfurther include the step of detecting surfaces with surface normalvalues greater than a planar region surface normal value threshold andthat are proximate to one another by less than a maximum proximitythreshold. The algorithm step of identifying a railway track bed featuremay further include the step of defining an approximate tie surfaceplane based on the detected surfaces with surface normal values greaterthan the planar region surface normal value threshold that are proximateto one another by less than the maximum proximity threshold.

The algorithm step of identifying a railway track bed feature mayfurther include the step of assigning a tie bounding box around theperimeter of the tie surface plane based at least on one measuredparameter of the tie surface plane. The algorithm step of identifying arailway track bed feature may further include the step of assigning anapproximate tie length, an approximate tie width, and an approximate tieskew angle based on the bounding box assigned around the perimeter ofthe tie surface plane. The algorithm step of identifying a railway trackbed feature may further include the step of identifying and measuringsurface cracks that are deeper than a minimum crack depth threshold andthat are longer than a minimum crack length threshold based on the trackelevation map. The data corresponding to the measured surface cracks maybe saved to the data storage apparatus on a per tie basis so that thesame tie can be re-examined at a later date to determine whether themeasured surface cracks have changed. The algorithm step of identifyinga railway track bed feature may further include a step of assigning aseverity value to each measured crack based on at least the measuredlength and measured width of the crack.

The algorithm step of identifying a railway track bed feature furthercomprises the step of identifying and measuring a surface feature thatis higher than a minimum tie height threshold. The data corresponding tothe measured surface feature may be saved to the data storage apparatuson a per tie basis so that the same tie can be re-examined at a laterdate to determine whether the measured surface feature has changed.

The algorithm step of identifying a railway track bed feature mayfurther include the step of detecting a broken tie based on an abruptelevation shift along the tie surface plane.

The algorithm step of identifying a railway track bed feature mayfurther include the step of comparing at least a portion of the trackelevation map to a plurality of three dimensional features saved in afeature library to determine a best fit between the at least a portionof the track elevation map and the plurality of three dimensionalfeatures to properly identify the railway track bed feature. Thealgorithm step of comparing may further include the step of applying aminimum correlation threshold so that a railway track bed feature willnot be identified as a particular three dimensional feature from thefeature library unless the minimum correlation threshold is met.

The algorithm step of identifying a railway track bed feature mayfurther include the step of determining a shoulder ballast volumeadjacent a tie based at least in part on the approximate tie surfaceplane defined for the tie.

The algorithm step of identifying a railway track bed feature mayfurther include the step of defining a surface area region adjacent thetie bounding box, measuring the surface elevation of the surface arearegion, and determining the difference between the surface elevation ofthe surface area region and the surface elevation of the approximate tiesurface plane to determine whether a positive volume or negative volumeis present at the surface area region.

The algorithm step of identifying a railway track bed feature mayfurther include the step of making a plurality of elevation measurementsalong and around an identified railway track bed feature and recordingthe measurements and the locations of the measurements in the datastorage apparatus. The algorithm step of identifying a railway track bedfeature may further include the step of assigning a condition to theidentified railway track bed feature based on the plurality of elevationmeasurements.

The algorithm step of identifying a railway track bed feature mayfurther include the step of measuring the length of a joint barcandidate, determining whether the length of the joint bar candidatefalls between a minimum joint bar length threshold and a maximum jointbar length threshold, and identifying the joint bar candidate as a jointbar if the length measurement of the joint bar candidate falls between aminimum joint bar length threshold and a maximum joint bar lengththreshold.

A system for assessing a railway track bed is disclosed, the systemcomprising a power source; a light emitting apparatus powered by thepower source for emitting light energy toward a railway track; a datastorage apparatus in communication with at least one processor; at leastone sensor for sensing reflected light that was emitted from the lightemitting apparatus and acquiring three dimensional image data of therailway track to be stored in the data storage apparatus, wherein theplurality of sensors are in communication with the at least oneprocessor; and the at least one processor wherein the at least oneprocessor includes an algorithm for extracting railway track bed surfaceelevation data to define new railway track bed components for a threedimensional track feature library, the algorithm comprising the stepsof: (a) acquiring three dimensional data representative of a segment ofrailway track bed; (b) generating a track elevation map based on theacquired three dimensional data; (c) identifying a railway track featurefrom the track elevation map that does not match any previously definedtrack features saved in a track feature library; (d) extracting threedimensional data from the track elevation map corresponding to theidentified railway track feature; (e) assigning a feature name to theextracted three dimensional data; and (f) saving in the data storageapparatus the extracted three dimensional data associated with thefeature name as a new track feature to be included in the track featurelibrary.

A method of building a virtual three dimensional railway track bedcomponent library is disclosed, the method comprising the steps ofemitting a light along a track bed surface; sensing some of the emittedlight after it has reflected off of the track bed surface; defining athree dimensional elevation map based on the sensed light reflected fromthe track bed surface; storing the elevation map in a data storageapparatus; identifying a railway track bed feature from the threedimensional elevation map that does not match any previously definedtrack bed features saved in a track component library; extracting threedimensional data from the track elevation map corresponding to theidentified railway track bed feature; assigning a component name to theextracted three dimensional data; and saving the extracted threedimensional data associated with the component name in a data storageapparatus as a new track bed feature to be included in the trackcomponent library.

A method of assessing a railway track bed is disclosed, the methodcomprising the steps of defining a three dimensional elevation map basedon data gathered by a sensor sensing reflected light from a track bedsurface; storing the elevation map in a data storage apparatus;identifying a railway track bed feature from the elevation map; andstoring information corresponding to the identified railway track bedfeature in the data storage apparatus.

The step of identifying a railway track bed feature may further includethe step of identifying a rail head edge by detecting significantvertical gradient edges over a two dimensional area wherein suchvertical gradient edges are greater than a minimum rail heightthreshold.

The step of identifying a railway track bed feature may further includethe step of identifying a rail base edge by detecting significantvertical gradient edges over a two dimensional area adjacent thedetected rail head edge wherein such vertical gradient edges are greaterthan a minimum rail base height threshold.

The step of identifying a railway track bed feature may further includethe step of comparing at least a portion of the track elevation map to aplurality of three dimensional features saved in feature library todetermine a best fit to properly identify the railway track bed feature.

The step of comparing may further include the step of applying a minimumcorrelation threshold so that a railway track bed feature will not beidentified as a particular three dimensional feature from the featurelibrary unless the minimum correlation threshold is met.

The step of identifying a railway track bed feature may further includethe step of measuring the length of a joint bar candidate, determiningwhether the length of the joint bar candidate falls between a minimumjoint bar length threshold and a maximum joint bar length threshold, andidentifying the joint bar candidate as a joint bar if the lengthmeasurement of the joint bar candidate falls between a minimum joint barlength threshold and a maximum joint bar length threshold.

The method of claim 26 further comprising the step of removing datacorresponding to the rail head from the elevation map, thereby enhancingthe detection of other smaller vertical components of the railway trackbed.

The method described above may further include the step of detectingsurfaces with surface normal values greater than a planar region surfacenormal value threshold and that are proximate to one another by lessthan a maximum proximity threshold. The method may further include thestep of defining an approximate tie surface plane based on the detectedsurfaces with surface normal values greater than the planar regionsurface normal value threshold that are proximate to one another by lessthan the maximum proximity threshold. The method may further include thestep of assigning a tie bounding box around the perimeter of the tiesurface plane based at least on one measured parameter of the tiesurface plane. The method may further include the step of assigning anapproximate tie length, an approximate tie width, and an approximate tieskew angle based on the bounding box assigned around the perimeter ofthe tie surface plane.

The method described above may further include the step of identifyingand measuring surface cracks that are deeper than a minimum crack depththreshold and that are longer than a minimum crack length thresholdbased on the track elevation map. The method may further include thestep of saving data corresponding to the measured surface cracks to thedata storage apparatus on a per tie basis so that the same tie can bere-examined at a later date to determine whether the measured surfacecracks have changed. The method may further include a step of assigninga severity value to each measured crack based on at least the measuredlength and measured width of the crack.

The method described above may further include the step of identifyingand measuring a surface feature that is higher than a minimum tie heightthreshold. The method may further include the step of saving datacorresponding to the measured surface feature to the data storageapparatus on a per tie basis so that the same tie can be re-examined ata later date to determine whether the measured surface feature haschanged.

The method described above may further include the step of detecting abroken tie based on an abrupt elevation shift along the tie surfaceplane.

The method described above may further include the step of determining ashoulder ballast volume adjacent a tie based at least in part on theapproximate tie surface plane defined for the tie. The method mayfurther include the step of defining a surface area region adjacent thetie bounding box, measuring the surface elevation of the surface arearegion, and determining the difference between the surface elevation ofthe surface area region and the surface elevation of the approximate tiesurface plane to determine whether a positive volume or negative volumeis present at the surface area region.

The method described above may further include the step of making aplurality of elevation measurements along and around an identifiedrailway track bed feature and recording the measurements and thelocations of the measurements in the data storage apparatus. The methodmay further include the step of assigning a condition to the identifiedrailway track bed feature based on the plurality of elevationmeasurements.

The summary provided herein is intended to provide examples ofparticular disclosed embodiments and is not intended to limit the scopeof the invention disclosure in any way.

BRIEF DESCRIPTION OF THE DRAWINGS

Further features, aspects, and advantages of the present disclosure willbecome better understood by reference to the following detaileddescription, appended claims, and accompanying figures, wherein elementsare not to scale so as to more clearly show the details, wherein likereference numbers indicate like elements throughout the several views,and wherein:

FIG. 1 shows a schematic view of a 3D track assessment system;

FIG. 2 shows an image of a full track width elevation and intensityprofile;

FIG. 3 shows a cross-sectional view of a rail and associated parts;

FIG. 4 shows an elevation and intensity profile including rails and anapproximated tie surface plane;

FIG. 5 shows an image including rails and ties and a slidingneighborhood used for track bed analysis;

FIG. 6 shows a flowchart including a protocol used for rail head edgedetection;

FIG. 7 shows an image of a track bed including a left rail field sidebase area, a left rail gage side base area, a right rail field side basearea and a right rail gage side based area;

FIG. 8 shows a flowchart including a protocol used for rail base edgedetection;

FIG. 9 shows a flowchart including a protocol for removing rail headfeatures from a 3D elevation map;

FIG. 10 shows an elevation map of a portion of a railway track bedbefore rail head data is removed;

FIG. 11 shows an elevation map of a portion of a railway track bed afterrail head data is removed;

FIG. 12 shows a rail base zone highlighting a vertical surface feature;

FIG. 13 shows a flowchart including a protocol for detecting rail baseweld features;

FIG. 14 shows an image or a track bed including areas with high surfacenormal values (flat planar regions) and light colored areas indicate lowsurface normal values (uneven or rough regions);

FIG. 15 shows an image of a track bed including large consolidatedplanar regions along the ties;

FIG. 16 shows an example of consolidated planar regions and a resultingapproximated tie surface plane;

FIG. 17 shows physical edge boundaries of a plurality of detected ties;

FIG. 18 shows a protocol for detecting and defining planar regions andfor defining tie bounding boxes for detected ties;

FIG. 19 shows a 3D elevation map for a section of track bed;

FIG. 20 shows an approximated tie surface plane (shaded area definedwithin the tie bounding box limits) overlaid on the surface of twowooden ties;

FIG. 21 shows a generated image including objects on a tie above the tiesurface plane and tie surface cracks along the tie;

FIG. 22 shows a generated image of a tie surface plane being used tohelp identify an end break along a tie;

FIG. 23 shows a generated image of a tie including a high center sectionindicative of a broken tie;

FIG. 24 shows a generated image of a cracked concrete tie;

FIG. 25 shows a protocol for detecting tie distress;

FIG. 26 shows 3D track assessment system configured to carry out amethod for identifying and analyzing 3D features along a railway trackbed;

FIG. 27 shows 3D feature model library examples including a wooden tiespike and a tie plate hole;

FIG. 28 shows feature model library examples including a first railanchor, a second rail anchor, and a third rail anchor;

FIG. 29 shows feature model library examples including a first PCC tiefastening clip, a second PCC tie fastening clip, and a third PCC tiefastening clip;

FIG. 30 shows an example of 3D model matching for a section of railwaytrack bed;

FIG. 31 shows a generated image of a railway track bed includingidentified features including plate holes, spikes, and anchors;

FIG. 32 shows a generated image of wooden ties with 3D track assessmentsystem 3D plate model outlines based on spike and hole template matchingresults;

FIG. 33 shows a protocol for detecting tie fasteners and anchors;

FIG. 34 shows an image schematically illustrating volume calculationsmade for a left shoulder volume, a left gage volume, a right gage volumeand a right shoulder volume;

FIG. 35 shows a protocol for calculating shoulder volumes;

FIG. 36 shows an image schematically illustrating ballast volumecalculations made along the perimeter of ties;

FIG. 37 a protocol for calculating the volumes of tie perimeter regions;

FIG. 38 shows a generated image of a crosstie bounding box defining tieseparation and skew angle parameters for individual crossties;

FIG. 39 shows a somewhat schematic view of a defined inter-crosstievolume between ties;

FIG. 40 shows a protocol for calculating ballast volume for the regionbetween and at the ends of ties;

FIG. 41 shows a generated image of a fastener and a number of relativeoffset measurement regions;

FIG. 42 shows a generated image including 3D elevation data for threeconcrete cross ties attached to rails;

FIG. 43 shows a protocol for determining pad thickness, rail seatabrasion, and insulator thickness;

FIG. 44 shows surface elevation data for a typical joint bar;

FIG. 45 shows elevation data including multiple identified featuresincluding a rail joint, a broken rail head, joint bars, and joint barbolts;

FIG. 46 shows a protocol for detecting joint bars and rail joints andprocessing associated data;

FIG. 47 shows a protocol for detecting cross tie plates;

FIG. 48 shows elevation data including a detected tie plates; and

FIG. 49 shows elevation data including a tie plate and specific zonesfor measuring vertical plate wear.

The figures are provided to illustrate concepts of the inventiondisclosure and are not intended to limit the scope of the inventiondisclosure to the exact embodiments provided in the figures.

DETAILED DESCRIPTION

Various terms used herein are intended to have particular meanings. Someof these terms are defined below for the purpose of clarity. Thedefinitions given below are meant to cover all forms of the words beingdefined (e.g., singular, plural, present tense, past tense). If thedefinition of any term below diverges from the commonly understoodand/or dictionary definition of such term, the definitions belowcontrol.

“Track”, “Railway track”, “track bed” or “railway track bed” is definedherein to mean a section of railway including the rails, ties,components holding the rails to the ties, components holding the railstogether, and ballast material.

A “processor” is defined herein to include a processing unit including,for example, one or more microprocessors, an application-specificinstruction-set processor, a network processor, a vector processor, ascalar processor, or any combination thereof, or any other control logicapparatus now known or later developed that is capable of performing thetasks described herein, or any combination thereof.

The phrase “in communication with” means that two or more devices are incommunication with one another physically (e.g., by wire) or indirectly(e.g., by wireless communication).

FIG. 1 shows a basic embodiment of a three-dimensional (3D) railwaytrack assessment system (“3DTAS”) 10 including a processor 12 incommunication with a light line projector 14 (e.g., a laser) and one ormore 3D sensors 16 for detecting light from the light line projector 14that is reflected from a railway track bed. The sensors 16 detectelevation and intensity data and the data is stored in a data storageapparatus 18 in communication with the processor 12. The data storageapparatus may include volatile memory, non-volatile memory, or acombination thereof. 3D elevation and intensity data is preferablystored in the data storage apparatus 18 as a separate file for eachsensor. Preferably, linear position references are logged for eachrecorded elevation profile using a high resolution distance measuringencoder 20 in communication with the processor 12. The encoder, shownschematically in FIG. 1, preferably operates at a rate of at least12,500 pulses per wheel revolution with a longitudinal distance ofapproximately 0.25 mm per pulse.

Preferably, a first sensor 16A is used to detect reflected light along afirst rail and a second sensor 16B is used to detect reflected lightalong a second rail. The data is then combined for both rails to providea full elevation and intensity profile of the full width of a railwaytrack bed as shown for example in FIG. 2. These full profiles are alsoreferred to as 3D elevation maps.

Following generation of full width 3D elevation maps, analysis includingautomated processing is completed to extract objective, repeatable, andaccurate measures for detected features of interest. This analysis canbe performed by the processor 12 or a separate processor separate fromthe system 10 by taking the data gathered by the system 10 and analyzingit. The identification of features is based on the definition andidentification of unique 3D feature attributes of a railway track bed asdiscussed in more detail below. Track beds can be simplified as beingcomprised of rails, crossties (ties), ballast, and other track materials(OTM) and crossings. The 3DTAS analysis approach is preferablyhierarchical, starting with the identification of the rails, railfeatures, ties, tie features, ballast, ballast features, and finally OTMand crossings.

From a 3D perspective, rails include rail heads 22 (normally the highestelevation in the track bed structure), joint bars 24 (for jointed railsections of track), and the rail base 26 as shown for example in FIG. 3.Once a rail head has been identified and located, the search regions forthe other rail components and features can be efficiently minimizedbased on proximity to the rail head.

The methodology for the identification of the rail head 22 is based onthe detection of significant (large vertical component) longitudinaledges over a 2D area. In the case of the 3DTAS methodology, a detected3D gradient magnitude for a given area must exceed a minimum rail heightthreshold (height of the detected edge above a calculated tie planesurface 28 as shown for example in FIG. 4). This edge analysis methodcalculates 3D gradient measures over zone of limited area referred to asa “neighborhood” that is applied in a sequential sliding and exhaustivefashion over the entire region to be processed. A suitable neighborhood30 as shown in FIG. 5 is preferably a 10 mm×30 mm gradient area. Railhead edges 32 are identified as those features with significant verticalgradient edges (e.g., a gradient greater than about 80 mm), with anelevation greater than a minimum height (e.g., about 100 mm) above theestimated plane of the tie surface 28.

Calculation of the 3D gradient and thresholding allows the unambiguousidentification of rail head edges as track features located above thecalculated tie plane surface 28 having elevation gradients greater thana minimum height, preferably, 100 mm. Left and right edge targets areidentified for both rails such that a first left rail edge 34A and asecond left rail edge 34B is identified for a left rail 36 and a firstright rail edge 38A and a second right rail edge 38B is identified for aright rail 40. This 3D gradient approach can be affected by atypicalvertical component conditions such as foliage, track bed debris, andhigh ballast. The rail edge targets with suitable vertical gradients arepreferably analyzed to identify outliers and eliminate those targetswhich are not located in valid rail edge lateral positions (based ondefined rail head dimensions for example) and are not collinear withother edge targets. This method of robust rail head edge detection isable to correctly identify rail head edges regardless of lateral shiftsin rail edge targets due to transverse test/survey vehicle movementsduring surveys (due to wide rail gauge or super elevated or curvedsections of track for example). In cases in which a rail head edge isnot detected, gaps in the detected rail head edges can be approximatedusing the valid edge measures before and after the missing segmentand/or as a calculated offset from the edge on the opposite side of therail head if the opposite edge has been detected.

The processing steps for the 3DTAS rail head edge detection are providedin FIG. 6. The process steps are carried out by a program stored on acomputer readable medium in communication with a processor. A first step(block 42) of the program includes inputting full width elevation datato a processor (e.g., the processor 12). An appropriate gradientneighborhood, is defined for vertical rail head edge features. This railhead edge neighborhood represents a small 2D track bed surface area overwhich differential vertical measurements are calculated (block 44), theexample given herein is an area of 10 mm×30 mm. The gradientneighborhood is applied by the processor to the 3D elevation data andthe area is moved like a window sequentially and completely for eachposition in the railway track bed elevation data (block 46). Theresulting vertical gradient map represents the maximum 3D gradient ateach elevation map measurement point. Vertical gradient values less thana predefined minimum rail height threshold are eliminated from thevertical gradient map, leaving only significant vertical gradientfeatures (block 48). A estimate of the crosstie surface elevationbetween the rails is calculated (block 50) based on a simple statisticalanalysis of the track bed elevation (median or mean for example) andusing typical rail dimensions to eliminate regions of the track bedsurface which are outside expected tie surface elevations (too high orlow). A rail head edge feature search zone is defined (block 52) basedon the 3D measurement sensor 14 position and the presence of featureswith significant elevations above the estimated crosstie plane elevation(for example elevations greater than one half of typical rail heightsabove the estimated tie plane surface elevation). The rail head searchzone is extended outward (made larger) to ensure no rail edge featuresare missed due to debris, vegetation or high ballast for example. Anycalculated 3D vertical gradients outside of the defined rail head searchzones are set to zero (eliminated) (block 54). Rail head edge targetsare defined as locations where the magnitude of the 3D gradient valuesexceed a minimum Rail Head Height Threshold (block 56). Continuous railedges are defined using standard interpolation methods to infill andremove outliers for each of the four linear (in the direction of travel)3D gradient edge target datasets (based on lateral position of thetargets relative to the 3D sensors 14A and 14C) representing the fieldand gage edges for the left and right rails (block 58). Followinginterpolation, the continuity of each rail head edge is verified (block60) and the detailed linear and spatial referencing for the 3D elevationand corresponding calculated gradient data are used to determinecoordinates from the rail head edges for both the left (block 62) andright (block 64) rails.

Once the rail head edges have been located, the 3D gradient is thenexamined separately for the field and gage side of each rail head. Thevalid field and gage rail base search areas are defined based onpre-defined distance offsets from the corresponding rail head edgelocations. The search areas include a left rail field side base area 66,a left rail gage side base area 68, a right rail field side base area 70and a right rail gage side based area 72 as shown in FIG. 7. Similar tothe approach used to locate the rail head edges, the rail base detectionuses 3D gradient detection and identifying vertical gradients greaterthan a specified height (about 25 mm for example) using a definedneighborhood 30 as a sliding window applied exhaustively across the railbase search area in the elevation data. Potential gradient targets aredisregarded for features outside of the search area and for trackcomponents with elevations not within a specified elevation range (e.g.,80 mm) above the estimated tie plane surface elevation 28. Field andgauge (left and right) rail base edge targets are identified for bothrails including a left rail field base edge region 74, a left rail gagebase edge region 76, a right rail field base edge region 78, and a rightrail gage base edge region 80.

This 3D gradient approach is affected by areas with insufficientgradients such as locations with ties beneath the rail base, andatypical conditions such as foliage, track bed debris, and high ballast.The rail base targets with suitable vertical gradients are preferablyanalyzed to identify outliers and eliminate those targets which are notlocated in valid rail base edge lateral positions (based on defined railbase dimensions for example) and are not collinear with other base edgetargets. This method of robust rail base edge detection is able tocorrectly identify rail base edges regardless of lateral shifts in baseedge targets due to transverse test/survey vehicle movements duringsurveys (due to wide rail gauge or super elevated or curved sections oftrack for example) or changes in rail type or dimensions. In cases inwhich a rail base edge is not detected, gaps in the detected base edgescan be approximated using the valid edge measures before and after themissing segment and/or as a calculated offset from the edge on theopposite side of the rail base if the opposite edge has been detected.

FIG. 8 shows a flowchart of processing steps for determining thelocation of rail base edges. The process steps are carried out by aprogram stored on a computer readable medium in communication with aprocessor. A first step (block 82) of the program includes inputtingfull width elevation data and previously determined rail head edgefeature coordinates to a processor. An appropriate gradientneighborhood, is defined for vertical rail base edge features. This railbase edge neighborhood represents a small 2D track bed surface area overwhich differential vertical measurements are calculated (block 84), theexample given herein is an area of 10 mm×30 mm. A rail base edge featuresearch zone is defined (block 86) based on the rail head edgecoordinates and maximum rail base widths, resulting in field and gagemaximum offset distances from the rail head. The gradient neighborhoodis applied by the processor to the 3D elevation data and the area ismoved like a window sequentially and completely for each position in therailway track bed elevation data (block 88). The resulting verticalgradient map represents the maximum 3D gradient at each elevation mapmeasurement point. Any calculated 3D vertical gradients outside of thedefined rail base search zones are set to zero (eliminated) (block 90).Small vertical gradient values within the search zone are eliminated,leaving only significant 3D gradient values which exceed a minimum RailBase Gradient Threshold (block 92). Continuous rail base edges aredefined using standard interpolation methods to infill and removeoutliers for each of the four linear (in the direction of travel) railbase edge target datasets (based on lateral position of the targetsrelative to the identified rail head edges) representing the field andgage rail base edges for the left and right rails (block 94). Followinginterpolation, the continuity of each rail base edge is verified (block96) and the detailed linear and spatial referencing from the 3Delevation and corresponding calculated gradient data are used todetermine coordinates for the rail head edges for both the left (block98) and right (block 100) rails.

In order for smaller features along a railway track bed to be moreeasily detected and categorized, it is preferable to remove rail headfeatures from the 3D elevation maps. As such, using a processor such as,for example, the processor 12 of the system 10, the 3DTAS 3D analysismethodology preferably removes rail web and rail head elevation data toenhance 3D feature detection capabilities. By artificially(mathematically) eliminating the rail head component from the 3D trackbed elevation maps, the 3D detection of the remaining smaller verticalcomponents is enhanced. Large vertical dimension components tend to masksmaller features in close proximity. In the case of fastening systems,rail base welds, and anchors, elimination of the rail head is paramountfor correct feature detection. This approach provides a significantperformance improvement in the reliable detection of all other track bed3D features of interest.

The rail head elimination process is detailed in FIG. 9. The processsteps are carried out by a program stored on a computer readable mediumin communication with a processor. A first step (block 102) of theprogram includes inputting full width elevation data and previouslydetermined rail head edge coordinates and rail base edge coordinates toa processor. Rail base surface zones are defined as the sections of thetrack bed elevation surface bounded by the rail head edges and rail baseedges (block 104). The locations of the track bed elevation mapcorresponding to the rail head, the elevations in the region bounded bythe left and right rail head edges for each rail, are set to NULL (block106). An appropriate sliding neighborhood, is defined for the rail basezones. This rail base surface neighborhood represents a small 2D surfacearea over which statistical measurements are calculated (block 108), forexample, an area of 10 mm×75 mm. The rail base surface neighborhood isapplied by the processor to the 3D elevation data for each of the fourrail base surface zones, one located on each side of both rails. Theneighborhood area is moved sequentially and completely, like a window,for each position in the four rail base zones and the lowest elevationmeasure for each neighborhood is determined at each position (block110). This process calculates the 2D neighborhood surface minimum foreach rail base zone. Surface elevations are then calculated (block 112)between the rail base surface zones on either side of each rail byinterpolating the minimum elevations calculated in block 110. Theinterpolated minimum elevations for each rail head zone (calculated inBlock 112) are used to infill the rail head zone elevations previouslyassigned NULL values (block 114). A sliding neighborhood smoothing(averaging) filter is applied to the rail head zone for each rail (block116). FIG. 10 shows an elevation map of a portion of a railway track bedbefore the rail head data is removed, and FIG. 11 shows the same sectionof track after the rail head elevation have been modified byinterpolating the rail base surfaces on either side.

A rail base zone 118 as highlighted, for example, in FIG. 12, can bedefined as the region between the rail head and rail base edges. Oncethe rail head/base edge detection has been completed, the rail baseregions can be defined. Any significant vertical surface features notattributable to a fastening system (for example not in the proximity ofa supporting crosstie or in proximity of a crosstie and not matching anyknown fastener type) can be identified as potential weld artifactfeatures 120 as shown, for example, in FIG. 12. Vertical surfacefeatures that deviate from a planar approximation of a typical rail basesurface are defined as vertical deviations. A planar surfaceapproximation is calculated as the surface generated from the collectionof minimum elevation values for a sliding neighborhood 122 (shown inFIG. 12) moving along the rail base surface region with a length greaterthan the feature of interest (for example 75 mm representing a length 3to 4 times longer than typical rail base weld features). Any featureswith a vertical deviation of greater than a system defined rail basedeviation threshold (about 5 mm in this example) detected for the entirerail base surface region width and occurring on both sides of the railare identified by the system 10 as a rail weld feature. The rail basevertical offset difference calculated from a first surface elevation 124and a second surface elevation 126 on either side of the weld locationare determined and retained for each weld location. This data isposition referenced and is stored in a data storage apparatus. Anysignificant difference of rail base surface elevations indicates avertical misalignment across the rail weld. Calculated and measured weldparameters include physical dimensions (height, width), rail basedifferential height across the weld, and distance from leading andtrailing ties (shown in FIG. 12). These parameters are retained for boththe left and right rail base surfaces and stored in a data storageapparatus.

FIG. 13 shows a flowchart detailing the process steps of the rail baseweld features detection method described above. The process steps arecarried out by a program stored on a computer readable medium incommunication with a processor. A first step (block 128) includesinputting various data sets to a processor including elevation datawherein significant elevations due to the rail heads for each rail havebeen removed, rail head edge coordinates, and rail base edgecoordinates. Rail base surface zones are defined as the sections of thetrack bed elevation surface bounded by the rail head edges and rail baseedges (block 130). An appropriate sliding neighborhood, is defined forthe rail base surface zones. This rail base surface analysisneighborhood represents a small 2D surface area over which elevationmeasurements suitable for weld detection are calculated (block 132), forexample, an area of 10 mm×75 mm. The rail base surface neighborhood isapplied by the processor to the 3D elevation data for each of the fourrail base surface zones, one located on each side of both rails. Theneighborhood area is moved sequentially and completely, like a window,for each position in the four rail base zones and the lowest elevationmeasure for each neighborhood is determined at each position. Thisprocess calculates the 2D neighborhood surface minimum for each railbase zone. Weld feature targets are defined as those localized regionswith surface elevations which are within maximum and minimum thresholdswith respect to the calculated minimum rail base surface elevations, andwhose length is within the maximum and minimum length threshold for weldfeatures (block 136). Following the identification of Weld Targets foreach separate rail base surface zone, the presence of targets on bothrail base surfaces on either side of each rail (block 138). Weld targetsnot occurring on both sides of either rail, are eliminated (block 140).Weld feature targets occurring on the rail base surfaces on both sidesof a rail, are paired as a single weld feature and physical parameters(location, length, width, height, proximity to crossties for example)are determined and stored (block 142). The rail base surface elevationsare analyzed on both sides of each weld feature and the elevationdifferential across the weld is calculated for both sides of each rail(block 144). This elevation differential data is stored with the otherphysical data for each weld feature.

Flat surface regions are a typical characteristic of constructedmaterials including many components of interest found in railway trackbeds. The ability to identify planar regions is required for manmadefeature identification and classification. The 3DTAS post-processingsystem uses a sophisticated approach to the identification of planarsurfaces including calculating the magnitude of a vertical surfacenormal component from a 3D surface gradient acquired from 3D elevationdata. The 3D gradient quantifies the variations in the surface elevationwithin a sliding neighborhood for an entire surface elevation map. Inthe example analysis included here, the localized 2D neighborhood overwhich the gradient is calculated is 5 mm transverse×15 mm longitudinal.Localized deviations in surface elevations produce significantvariations in localized gradient values, which produce low verticalsurface normal values. Planar regions produce insignificant verticalgradient variations which results in significant or large verticalsurface normal values.

Calculating vertical surface normal values allows the efficientdifferentiation between manmade features and natural features of a trackbed 3D surface elevation map. In particular this method effectivelydifferentiates between the natural ballast stone and ties, plates andrails. FIG. 14 shows the results of 3DTAS planar region analysis using3D surface elevation gradient and surface normal calculations. Thesection of track bed in this example includes three wooden ties 148,with the rails removed from the data (by eliminating the track bedelevation data for zones defined between the rail base edgedefinitions). In FIG. 14, dark regions indicate areas with high surfacenormal values (flat planar regions) and light colored areas indicate lowsurface normal values (uneven or rough regions).

The planar region analysis described herein consolidates all significantregions (i.e., regions with greater than a minimum surface areathreshold) with high surface normal values (i.e., surface normal valuesgreater than a planar region surface normal threshold) that are in closeproximity to one another (i.e., less than a maximum proximitythreshold). Large consolidated planar regions 150 are shown, forexample, in FIG. 15. Following the consolidation of the planar regions,the elevation values are used to generate an approximating surfaceplane. In the case of wood tie track bed sections, the tie plate regionsaround each rail are preferably excluded. One example of consolidatedplanar regions and the resulting approximated tie surface plane 152 isshown in FIG. 16.

The surface plane closely approximates a new tie surface and the planarapproximation is used to identify other track features and calculateparameters of interest. These features include tie bounding boxdefinitions (including tie physical dimensions such as length, width andskew angles), fastening systems, and tie condition. To acquire a tiebounding box definition the consolidated planar regions are preferablycombined with the surface plane approximation shown in FIG. 16 andstandard tie physical models (length and width parameters) to producethe physical edge boundaries of each detected tie (a tie bounding box154) as shown in FIG. 17. The bounding box definition method includesedge quality measures (to determine how linear each planar surface basedtie edge is) and industry standard tie models to assist in correctlydefining and orienting the bounding box in cases where the tie isdamaged or broken.

FIG. 18 shows a flow chart including the method steps for detecting anddefining planar regions and for defining tie bounding boxes. The processsteps are carried out by a program stored on a computer readable mediumin communication with a processor. The method includes a first step(block 156) of inputting various data sets to a processor includingelevation data wherein the rail head data has been removed, rail headedge coordinates, and rail base edge coordinates. Using the rail baseedge definitions for both rails, the elevation data for the regionbounded by the rail base edges for both rails is set to NULL producingan elevation map (NoRailElev) that excludes the track bed rails fromsubsequent planar region processing (block 158). An appropriate slidingneighborhood, is defined for the smooth surface analysis. This railsmooth surface analysis neighborhood represents a small 2D surface areaover which elevation measurements suitable for planar surface detectionare calculated (block 160), for example, an area of 15 mm×15 mm. Thesmooth surface neighborhood is applied by the processor to the 3Delevation data with the rail region removed, to calculate the verticaldeviation in each neighborhood. The neighborhood area is movedsequentially and completely, like a window, for each position and theelevation gradient for each neighborhood is determined at each position(block 162). Following the calculation of the track bed verticalgradient, a second sliding neighborhood based processing operation isperformed to calculate the vertical surface normal vector for theelevation gradient map (block 164). Surface normal for elevationgradients produce larger vertical surface normals for flat surfaces andlower normals for rough or uneven surfaces. Planar track bed areascorrespond to those areas which have a normalized surface normal valuewhich exceeds a defined threshold (block 166), 0.75 for example. FIG. 14shows an example of a segment of track bed containing three crosstieswhich has been processed to highlight the plan areas. Small planarregions are subsequently discarded by eliminating those regions with asurface area less than a minimum area threshold (block 168). Furtherprocessing eliminates planar regions which are both isolated and have asurface area which is less than a minimum isolate region size threshold(block 170). An example of the results of this small region processingare shown in FIG. 15. If the track section includes wooden ties,estimated tie plate regions are defined and planar areas located inthese plate regions are eliminated (block 172 and 174). Followingconsolidation or clustering of close proximity planar sections, Best Fitprocessing (block 176) of all available crosstie models are completedfor each clustered planar region to define the best match tie model, asshown in FIG. 15. Following the selection of the best fit tie model foreach set of clustered planar regions, the corresponding best fitbounding box (including position skew and lateral offset) is defined, asshown in FIG. 16. Once defined, the bounding box limits and planarregion elevations are used to calculate the minimum least squared fitplane approximation of the collection of planar regions within thebounding box. This approximating planar surface represents anapproximation for a like-new tie surface which is used to calculate tiephysical parameters and to assess tie condition, as shown in FIG. 17.

Following planar region analysis, the calculation of a tie surface planeapproximation and the definition of a tie bounding box, a detailed tiecondition analysis is possible. The 3DTAS 3D tie condition assessmentuses 3D deviations from an as-new tie condition estimate to objectivelyand accurately quantify and assess the current condition of a tie.

Given a 3D elevation map for a section of track bed 190 (as shown forexample in FIG. 19), the planar analysis allows the calculation of anapproximating tie surface plane and bounding box using the methoddescribed above. FIG. 20 shows an approximated tie surface plane 192(shaded area defined within the tie bounding box limits) overlaid on thesurface of two wooden ties 194. The tie surface plane 192 preferablyexcludes the rail and tie plate surfaces and includes the planarportions of the tie surface. Features which deviate vertically (by morethan separate above surface and below surface vertical deviationthresholds) from this ideal surface can be identified and quantified.Objects on the tie above the tie surface plane 192 represent features onthe surface of the tie such as ballast or debris 196 shown in FIG. 21.These features obscure the surface and the total hidden tie surface arearepresents an important parameter during tie condition assessment.Detected areas of the tie below the tie surface plane 192 (surfacevoids, or cracks) represent significant tie defects. The 3DTAS tiecondition assessment method described herein identifies, locates andmeasures all surface cracks (voids) deeper than a minimum crack depththreshold and longer than a minimum crack length (extent) threshold.Examples of crack features 198 are shown in FIG. 21.

Each detected crack is analyzed for all 3D surface elevation pointsbelow the tie surface. Information recorded for each crack featureincludes surface area (the area defined by the number of connectedsurface elevation measurement points forming the crack in its entirety),crack depth (min, max, mean and median deviation from the estimated tiesurface plane to the depth at each crack measurement point), cracklength (measured along the path of the crack), crack width (min, max,mean and median crack width for all points along the length of thecrack), crack orientation (start point, end point, and the straight lineapproximation for the crack), and the crack location (defined by whereon the crosstie the crack occurs; for example on either tie end, or thetie center between the rails). These parameters are used to establish anaccurate and objective severity and extent distress measures for eachcrack. The severity determination includes additional rules forpenalizing end break cracks, and orientations which pass through spikelocations (and further penalizes if the affected spike height is above anominal height threshold representing an unseated or elevated spikehead). Crack severity is further increased if a crack extends from a tieend under the tie plate to the center section of the tie.

The tie surface plane 192 is also employed to identify end breaks 200(missing portions of tie ends as shown for example in FIG. 22), andbroken ties 202 having high center sections or high ends as shown forexample in FIG. 23. Broken ties are ties wherein the plane of the tiesurface abruptly changes in a vertical sense a 3D elevation map,indicating the tie is broken. Similar surface plane analysis techniquesare used to detect anomalies for concrete ties. At the time ofmanufacture, concrete ties have planar regions at many regions on thetop surface including both ends of the crosstie with known as-designedorientations. Using surface normal calculated for the tie end surfaceplanes to identify deviations from as-designed specifications helpidentify broken or cracked crossties as is shown, for example, with thebroken tie in FIG. 24. A differential tie end surface normal analysismethodology is an effective and reliable method for identifying brokenties regardless of installed tie orientation.

A tie distress detection method flowchart is shown in FIG. 25. Theprocess steps are carried out by a program stored on a computer readablemedium in communication with a processor. A first step (block 220) ofthe program includes inputting data sets to a processor, such data setsincluding 3D elevation map data with the data associated with the railheads removed, elevation map sample resolution (longitudinal andtransverse), rail base edge coordinates, detected tie bounding box data,and approximate tie surface plane data. In order to objectively assessthe condition of each crosstie, the current condition of the tie iscompared to an as-new tie condition. This comparison is accomplished bycalculating the difference between the high resolution 3D elevation mapdata for each crosstie and the new crosstie surface approximationprovided by the Tie Surface Plane models calculated for each crosstiesurface bounded by the corresponding tie bounding box (block 222). Theresulting difference surface emphasizes non-planar features of interest.Features identified above the plane of the crosstie (producing apositive elevation difference) represent items or materials on thesurface of the crosstie. Positive elevation difference regions with amagnitude greater than Ballast Height Threshold are identified asBallast Obscured Areas (block 224) and represent areas where thecrosstie surface condition cannot be assessed. Ballast Obscured Areadetails (location, extent, elevation for example) are retained andstored for each crosstie. Negative elevation difference regions, with amagnitude greater than Crack Depth Threshold, represent Crosstie CrackTargets (block 226). Crosstie Crack Targets are 3D features havinglength, width and depth. Crack Targets having a short length or smallsurface area are eliminated from further analysis (block 228). Theremaining Crosstie Crack Targets are analyzed, producing comprehensivephysical parameters (block 230). These crack parameters can includelocation, surface area, crack volume, length, width, zone (tie end,between rails), and fastener proximity for example. These attributes areused to define and/or modify crack severity and extent values assignedto each crack. All parameters are retained and stored for each tie. Thearea of all crosstie cracks present in each tie end zone is accumulated.If the accumulated crack area exceeds the end zone Cracked AreaThreshold the corresponding Tie End Zone is designated a Broken Tie End(block 232). Broken Tie End status is retained and stored for each tie.The tie surface between the rails is analyzed to determine if thesurface deviates either above or below a planar surface approximation.If the tie surface deviates from the Tie Plane Model by an amountgreater than Broken Tie Threshold, the crosstie is designated as CenterBroken (block 234), and this status is retained and stored for eachcrosstie. If the crosstie being analyzed is concrete (block 236) theorientation of each tie end zone surface is calculated (using theorientation of the surface normal for each end for example), and theseorientations are compared to determine if the measured orientationsdeviate by more than Surface Normal Angle Threshold from the as-designedorientations. Any deviation of the tie end surface plane orientationfrom the as-designed orientation by more than this threshold signifies abroken tie, and this status is retained and stored for each crosstie.All accumulated Tie Distress Parameters are stored with the associatetie from which the parameters were derived.

In a preferred embodiment, a 3DTAS system 242 includes a processor 244,a data storage apparatus 246 in communication with the processor, one ormore computer programs 248 stored on a computer-readable medium 250, anda 3D feature library 252 stored on the computer-readable medium 250 asshown schematically in FIG. 26. In some embodiments, the system 242includes one or more 3D sensors, one or more light line generators andat least one encoder, all of which are in communication with theprocessor 244. In some embodiments, the computer-readable medium 250 andthe data storage apparatus 246 are the same apparatus. The system isconfigured to carry out a method for identifying and analyzing 3Dfeatures along a railway track bed. The 3DTAS discrete 3D featureidentification and analysis methodology is a surface elevation model(3D) block matching technique using normalized frequency domaincross-correlation (Fast Fourier Transform based). As new trackcomponents are identified, representative regions of the track surfaceelevation data containing the new feature are extracted to develop 3Dfeature models. These new 3D models are added to the system featurelibrary 252. Track feature libraries have been developed for both woodenand PCC tie components.

FIG. 27 shows 3DTAS 3D feature model library examples including a woodentie spike 254 and a tie plate hole 256. FIG. 28 shows feature modellibrary examples including a first rail anchor 258, a second rail anchor260, and a third rail anchor 262. FIG. 29 shows feature model libraryexamples including a first PCC tie fastening clip 264, a second PCC tiefastening clip 266, and a third PCC tie fastening clip 268.

The 3DTAS 3D feature identification system described herein limits theprimary feature search to target areas centered along each of the rails.These zones preferably represent rail fastener locations. Using theprocessor 244, each appropriate 3D feature from the 3DTAS featurelibrary 252 is automatically template matched against an entire surfaceelevation map for the applicable region of the track bed. An objectivecross-correlation coefficient is determined for the entire testedsurface area. Each area is tested in turn, and the highest normalizedcross-correlation value at each location on the track surface for eachlibrary feature determines the identity of the feature. There is aminimum correlation threshold which must be exceeded for any target tobe identified and classified as a specific rail feature.

An example of the 3D model matching for a section of track is shown inFIG. 30. The left panel 270 shows the rail head removed elevation mapfor a segment of track bed containing a section of rail in the centerand two concrete crossties and SafeLok fasteners. The middle panel 272presents a graphical rendering of the results from 3D template matchinga gage (left) side oriented 3D SafeLok model with the elevation map inthe left panel 270. The dark areas identify the centroid locations ofhighly correlated 3D models, white areas indicate no model correlationwith the elevation map and darker areas signify better model matchquality at those points. The right panel 274 presents the graphicalrendering of the 3D Template Matching results of the 3D SafeLok fastenermodel oriented in the field (right) side configuration with theelevation map in the left panel. These middle and right panelsdemonstrate the ability of the 3D Template Matching approach todifferentiate different orientations of the same model. The location, 3Dmodel correlation quality, and model type are recorded in the datastorage apparatus 246 for all match targets.

The 3DTAS feature identification system was applied to the track bedexample shown in FIG. 21 for all track bed features stored in thefeature library 252. The identified features are shown in FIG. 31including plate holes 300, spikes 302, and anchors 304. Followingidentification of each feature type, physical parameters specific tothat feature are determined. For example, for spikes, spike height,number of spikes, and plate location (e.g., field/gage, on/off railbase) are preferably determined. For anchors, the type and longitudinaloffset from the nearest tie is determined. Additionally, the number andlocations of spikes and holes are used to identify plate size and type.The 3DTAS 3D feature library includes models for many plate types. FIG.32 shows wood ties 306 with 3DTAS 3D plate model outlines 308 based onspike and hole template matching results.

A tie fastener and anchor detection method flow chart is shown in FIG.33. The process steps are carried out by a program stored on a computerreadable medium in communication with a processor. A first step (block310) of the program includes inputting data sets to a processor, suchdata sets including 3D elevation map data with the data associated withthe rail heads removed, elevation map sample resolution (longitudinaland transverse), rail base edge coordinates, detected tie bounding boxdata, and the 3D feature library 252. Comprehensive 3D Model TemplateMatching is used to identify the presence of 3D features contained inthe 3DTAS library. In order to improve template matching efficiencies,the feature libraries are subdivided based on crosstie type. Currentlibraries include both generic and railroad specific fastening andanchor system components for wood and concrete ties. Prior to beginningthe feature template matching analysis, the active Feature Library isassigned based on the type of crosstie being analyzed (block 312). Forwooden ties the active Feature Library is set to the WoodTieFeaturelibrary of 3D track component models (block 314), and for concretecrossties, the Feature Library is set to the 3DTAS PCCTieFeature library(block 316). At the time the feature library is assigned, the fastenersearch offset is assigned to optimize the 3D template matchingoperation. This offset represents the lateral offset from thecorresponding rail base edge that is included in the component matchzone, WoodTieOffset (block 314) for wood ties, and PCCTieOffset (block316) for concrete ties. Following feature library and search offsetassignments, each of the corresponding fastener and anchor 3D modeltypes are tested against the entire track bed 3D elevation map. Anotherapproach uses two smaller sections of the entire track bed 3D elevationmap for efficiency. These sections can be defined by the rail base edgesand the TieOffset parameters, producing two subsections of the elevationmap centered about each of the rails (a segment of one of thesesubsections is shown in FIG. 30). Each of the fastener features in theactive Feature Library are tested against the track bed elevation map insequence (block 320). The results of this 3D Model Template Matchingprocess are analyzed, and fastener template matching scores which areless than the 3D Fastener Correlation Threshold are eliminated (block322). Physical parameters including fastener type, location, 3D modelmatch quality, associated tie bounding box, orientation, centroid basedfastener neighborhood reference point elevations are calculated andstored for each fastener target (block 324). Each of the anchor featuresin the active Feature Library (block 326) are tested against the trackbed elevation map in sequence (block 328). The results of this 3D ModelTemplate Matching process are analyzed, and anchor template matchingscores which are less than the 3D Anchor Correlation Threshold areeliminated (block 330). Physical parameters including anchor type,location, 3D model match quality, closest proximity tie bounding box,rail base edge proximity and orientation are calculated and stored foreach anchor target (block 332). Any additional 3D features of interestwould be 3D Template Matched, and the results analyzed and retained in asimilar fashion. Tie fastener and anchor targets location and relatedphysical parameters and reported and stored (block 334).

The 3D track surface elevation data is also used to define ballastprofile measurements for both the shoulder and on the leading andfollowing edges for each tie following the determination of individualtie bounding boxes. The 3DTAS is capable of calculating and reportingshoulder volumes at any client specified distance interval along a trackbed (max, min, mean volumes per mile for example) as shown, for example,in FIG. 34 wherein the longitudinal distance is set, for example, at 1meter. Calculations are preferably made for a left shoulder volume 368,a left gage volume 370, a right gage volume 372 and a right shouldervolume 374 based on any set longitudinal distance interval 366 and a setfield width 376 and gage width 378.

The detailed processing steps for the shoulder volume calculationmethodology is provided in FIG. 35. The process steps are carried out bya program stored on a computer readable medium in communication with aprocessor. A first step (block 380) of the program includes inputtingdata sets to a processor, such data sets including 3D elevation map datawith the data associated with the rail heads removed, elevation mapsample resolution (longitudinal and transverse), rail base edgecoordinates, detected tie bounding box data, and a distance reportinginterval. Ballast volumes are referenced to an approximate track bedsurface plane calculated from a least squares fit to all of the crosstiesurfaces defined within the 3D elevation map region being analyzed. Thisballast volume analysis is conducted sequentially, at any discretelongitudinal reporting interval, from the start to the end of theelevation map. Prior to processing, the ballast volume analysisestablishes the start point (block 382) and endpoint based on the startpoint and the defined volume analysis reporting interval (block 384).The elevation map subsection defined from the StartPOS to the EndPOS andcorresponding to the length of defined reporting interval is extractedand retained (block 386). Elevation measurements from this volumeanalysis subsection corresponding to locations within tie bounding boxesare least square fitted to a planar surface (block 388), representing atrack bed reporting interval Reference Plane approximation. The outsiderail (Field) and between rail (Gage) zones of the reporting interval aredefined (block 390) using the rail base edges. The ballast elevation ineach zone is calculated (block 392) and these elevations are subtractedfrom the previously calculated track bed Reference Plane (block 394).Elevation Differences which are positive represent ballast levels abovethe top of tie reference plane, and negative elevations representballast levels below the approximating tie surface. The elevation map 2Dsurface area cell size is defined as the rectangular area defined by theLongitudinal and Transverse Sample Spacing distances (block 396). Theindividual interval zone volumes are then determined by multiplying andaccumulating the calculated elevation difference at each point withineach zone by the 2D cell area. The volume for each of the shoulder(field) zones and for the zone between the rails (gage) are calculatedand retained (block 398). The interval endpoints are shiftedlongitudinally and if the interval has not reached the end of the 3Delevation map the process is repeated for the next interval (block 400).All of the calculated ballast volumes, for each zone in each sequentialreporting interval are reported and retained (block 402).

A similar approach is used to calculate the up chainage (leading)/downchainage (trailing) tie edge volumes, based on ballast regions offsetfrom each tie bounding box. The 3DTAS defines surface area regionsadjacent to each tie bounding box that are used to calculate ballastvolumes. Such volumes include leading edge volume 408, trailing edgevolume 410, left shoulder volume 412 and right shoulder volume 414.These volumes are defined in part by a set tie trailing edge width 416,a tie leading edge width 417, a tie left shoulder width 418 and a tieright shoulder width 419. These volumes are also defined in part by aleft tie field length 420, a tie center length 421, and a tie rightfield length 422. These volumes are calculated as the difference betweenthe measured surface elevation for each of the defined ballast volumeregions and the surface plane calculated from the surface of each tie(shown in FIG. 36). Differential ballast levels (volumes) around theperimeter of each tie are of interest to rail infrastructure owners. Inparticular, situations where a tie is anchored to a rail and is beingdragged (being plowed) by stresses occurring in the rail which causepositive ballast volumes in the direction the tie is being dragged andnegative volumes on the other side of the tie. A positive volumeindicates that ballast levels are above the tie plane surface (berms),and negative volumes for areas with the ballast levels below the tiesurface (voids). Ballast volume surface area zone definition parametersare configurable within the 3DTAS environment (typical zone widths arepreferably about 100 mm, and zone lengths are preferably defined by theapplicable tie length). All volumes are preferably recorded on atie-by-tie basis, and the aggregate (max, min, mean, and median) volumescan be accumulated and reported for any longitudinal interval of track(e.g., based on distance or number of ties).

FIG. 37 includes a flow chart detailing process steps for a tieperimeter regions volume calculation method. The process steps arecarried out by a program stored on a computer readable medium incommunication with a processor. A first step (block 424) of the programincludes inputting data sets to a processor, such data sets including 3Delevation map data with the data associated with the rail heads removed,elevation map sample resolution (longitudinal and transverse), rail baseedge coordinates, detected tie bounding box data, and leading regionwidth, trailing width, and tie end region width. Crosstie perimeterballast volumes are referenced to a surface plane approximationcalculated from a least squares fit of each crosstie surface. Thecrosstie perimeter ballast volume analysis is conducted sequentially,for each crosstie bounding box defined within the elevation map.Processing begins with the first crosstie bounding box defined withinthe elevation map. The elevation map measurements corresponding tolocations within tie bounding box are least square fitted to a planarsurface (block 426), representing a crosstie surface Reference Planeapproximation. The leading edge (forward direction), trailing edge(reverse direction) region widths are used to define zones along theleading and trailing edges of the tie bounding box (block 428). Theballast elevation in the leading and trailing edge zones are calculated(block 430) and these elevations are subtracted from the previouslycalculated crosstie surface Reference Plane (block 432). ElevationDifferences which are positive represent ballast levels above the top oftie reference plane, and negative elevations represent ballast levelsbelow the approximating tie surface. The elevation map 2D surface areacell size is defined as the rectangular area defined by the Longitudinaland Transverse Sample Spacing distances (block 434). The individualleading and trailing zone volumes are then determined by multiplying andaccumulating the calculated elevation differences at each point withineach zone by the 2D cell area. The ballast volume for the leading andtrailing edge zones are calculated and retained (block 436). The tie endedge region widths are used to define zones along the left (surveydirection) and right tie end edges of the tie bounding box (block 438).The ballast elevation in the left and right tie end edge zones arecalculated (block 440) and these elevations are subtracted from thepreviously calculated crosstie surface Reference Plane (block 442). Theindividual left and right tie end edge zone volumes are then determinedby multiplying and accumulating the calculated elevation differences ateach point within each zone by the 2D cell area. The ballast volume forthe left end and right end edge zones are calculated and retained (block444). The crosstie bounding box index is incremented and if the boundingbox index does not exceed the number of bounding boxes, the process isrepeated (block 446). All of the calculated leading, trailing, left edgeand right edge ballast volumes, are associated and retained with eachcorresponding crosstie bounding box (block 448).

Another feature critical to the stability of railway track beds is theintegrity of the crosstie to ballast interface. High quality ballast,adequately tamped (compacted) and placed at the correct levels,effectively transfers both vertical and lateral loads to the track bedsub-structure. Areas with insufficient ballast in the crib and shoulderareas represent areas with the potential for diminished track stabilityand are of interest to railway owners and operators.

Following the identification of tie planar surface regions, and thecorresponding definition of individual crosstie bounding boxes, thetrack bed surface can be segmented into crosstie region 454, cribballast region 456 and shoulder ballast region 458 as shown in FIG. 38.The calculation of all crosstie bounding boxes allows the definition oftie separation and skew angle parameters for individual crossties suchas, for example, the tie bounding box 460 and skew angle 462 for the tie464 shown in FIG. 38. These physical parameters are preferably combinedwith tie size (length and width) and location (linear and spatialreferenced) for each crosstie. The crib area (inter-tie) and theshoulder area ballast levels can then be calculated continuously alongthe track.

The inter-crosstie volume is defined as the difference between a planecalculated from the leading and trailing crosstie surfaces (shadedsurface 466 in FIG. 39), and the ballast surface in the sameinter-crosstie region. Negative volumes represent regions with ballastsurface elevations which are below the plane of the tie surfaces, andpositive volumes indicate regions with ballast surface elevations abovethe tie surface plane. A neutral volume region represents an area wherethe ballast surface is within a neutral ballast volume threshold (smallnominal positive/negative volume) of the crosstie surface plane level.

The left and right shoulder volumes are calculated as individual cells468 for the field region of the track bed beyond the ends of thecrossties, with any specified fixed longitudinal calculation andreporting distance defined by the 3DTAS shoulder ballast volume distanceparameter (0.6 meter for example). The shoulder volume surface area cellsize is defined by the maximum track bed profile measurement width andthe crosstie length (defining the cell width). The shoulder volumes arecalculated as the difference between the tie surface planes with the tiebounding boxes extended to the end of the field side scan regions andthe surface elevation of the shoulder ballast in each shoulder cell(shown as the alternating shaded regions in FIG. 39).

The calculated ballast volume parameters for each shoulder cell 468 andinter-crosstie (crib) region 456 are reported based on track positionand corresponding nearest proximity tie. Crib volumes, leading andtrailing edge volumes and tie skew angles are analyzed and exceptionsare reported. Exceptions include significant volume differences betweenleading and trailing volumes and high skew angles. The exceptionalvolume differences are defined by exceeding a 3DTAS volume differencethreshold.

The detailed processing steps for ballast volume calculations for theregion between and at the ends of each tie are detailed in the flowchartshown in FIG. 40. The process steps are carried out by a program storedon a computer readable medium in communication with a processor. A firststep (block 470) of the program includes inputting data sets to aprocessor, such data sets including 3D elevation map data with the dataassociated with the rail heads removed, elevation map sample resolution(longitudinal and transverse), rail base edge coordinates, and detectedtie bounding box data. Inter-crosstie (crib) and end region ballastvolumes are referenced to a surface plane approximation calculated froma least squares fit for two adjacent crosstie surfaces. The crosstiecrib and end region ballast volume analysis is conducted sequentially,for each pair of consecutive crosstie bounding boxes defined within theelevation map. Processing begins with the first pair of crosstiebounding boxes defined within the elevation map. The elevation mapmeasurements corresponding to locations within the two tie bounding boxare least square fitted to a planar surface (block 472), producing acrosstie surface Reference Plane approximation. The crib region isdefined as the ballast zone delineated by bounding box (BNN) leadingedge and bounding box (BNN+1) trailing edge and the minimum boundingboxes (BNN and BNN+1) left and right end edges (block 474). The ballastelevation in the inter-crosstie crib zone are calculated (block 476) andthese elevations are subtracted from the previously calculated crosstiesurface Reference Plane (block 478). Elevation Differences which arepositive represent ballast levels above the top of tie reference plane,and negative elevations represent ballast levels below the approximatingtie surface. The elevation map 2D surface area cell size is defined asthe rectangular area defined by the Longitudinal and Transverse SampleSpacing distances (block 480). The inter-crosstie crib zone volume isthen determined by multiplying and accumulating the calculated elevationdifferences at each point within the crib zone by the 2D cell area. Theballast volume for the inter-crosstie zone is calculated and retained(block 482). The left shoulder zone for bounding box (BNN) is defined asthe region from the left edge of the 3D elevation map to average ofbounding boxes (BNN and BNN+1) left edges horizontally, and from themidpoint between the leading edge of bounding box (BNN−1) and thetrailing edge of bounding box (BNN) to the midpoint between the leadingedge of bounding box (BNN) and the trailing edge of bounding box (BNN+1)longitudinally. The right shoulder zone for bounding box (BNN) isdefined as the region from the average of bounding boxes (BNN and BNN+1)right edges to right edge of the 3D elevation map horizontally, and fromthe midpoint between the leading edge of bounding box (BNN−1) and thetrailing edge of bounding box (BNN) to the midpoint between the leadingedge of bounding box (BNN) and the trailing edge of bounding box (BNN+1)longitudinally (block 484). The ballast elevation in the left and rightshoulder zones are calculated (block 486) and these elevations aresubtracted from the previously calculated crosstie surface ReferencePlane (block 488). The individual left and right shoulder zone volumesare then determined by multiplying and accumulating the calculatedelevation differences at each point within each zone by the 2D cellarea. The ballast volume for the left end and right shoulder zones arecalculated and retained (block 490). The crosstie bounding box index isincremented and if the bounding box index does not exceed the number ofbounding boxes, the process is repeated (block 492). All of thecalculated inter-crosstie, left and right shoulder ballast volumes, areassociated and retained with each corresponding crosstie bounding box(block 494).

Following the 3D analysis and identification of all rail fasteningsystems for a given section of railway track bed, the results of theidentification process provide the accurate position of every trackfastening component. Once a fastener location is known, the 3DTAS isable to extract elevation measurements in small regions relative to thegeometric center of each fastener. An example of a number of relativeoffset measurement regions (21 measurement regions identified by # and anumeral) for a Safelok III fastener 500 is shown in FIG. 41. For each ofthese measurement regions, a number of surface elevation measures arecalculated (min, max mean, and median values) and recorded. Any numberof operations are possible based on these measures, providing theability to monitor and report the in-situ performance of the entirefastener system and the fastener/crosstie interface.

Critical measures for the safe operation of a concrete crosstie basedtrack system include broken or missing fasteners, fastener insulatorwear, pad wear and rail seat abrasion. With the ability for accurate andrepeatable elevation measures at any arbitrary location referenced to afastener, all of these critical measures are possible. FIG. 42 shows 3Delevation data for three concrete cross ties 502 attached to rails 504.The rails 504 are attached to the cross ties 502 by fasteners 506.Following the identification of each fastener (using the 3DTAS 3Dfeature identification methodology described above), any number ofelevation measures can be determined. A plurality of elevationmeasurements are taken along a first fastener 506A, such measurementsdesignated by letters A-H. The minimum, maximum, mean and medianelevation measures are calculated for a localized neighborhood centeredabout each measurement point, determined relative to the center of thefastener 506A location, the measurement origin. For example, in FIG. 42,the elevation measures at locations A, B, and C represent the left fieldtop of crosstie elevation, locations E and F represent the field railbase elevation and G and H represent the top of fastener 506A insulatorelevations. These measures, combined with knowledge of the installedrail type (including as designed rail base thickness) and clip designparameters (clip toe insulator thickness), allow accurate in-situ padand insulator wear measurements.

Using the plurality of neighborhood based elevation measures in closeproximity to each detected fastener allows the calculation of a varietyof track infrastructure measures critical for effective and safeoperation of the railway. Although the actual measurement points(relative to the center of each fastener) will vary for differentfastener types, the elevation parameters measured remain the same. Forexample, for concrete ties these elevation parameters include; Top ofTie Elevation (FIG. 42 reference points A, B and C on fastener 506A),Top of Rail Base Elevation (FIG. 42 reference points E and F on fastener506A), and Top of Fastener Elevation (FIG. 42 reference points G, H andI on fastener 506A). Using these elevation measures, knowledge of thedesign rail base thickness (as shown in FIG. 3) and knowledge offastener dimensions, then Rail Pad Thickness, Rail Base to Top of TieClearance (Rail Seat Abrasion measures), and Fastener Top of Toe to Topof Rail Clearance (Insulator Wear measures) can be determined, forexample, using the following calculations:

Rail PadThickness=mean(Elev_(E),Elev_(F))−mean(Elev_(A),Elev_(B),Elev_(C))−RailBase Thickness

When the Rail Pad Thickness measure diminishes to 0, the bottom of therail base is in direct contact with the Top of Tie, allowing Rail SeatAbrasion to occur. Therefore, Rail Seat Abrasion is reported when RailPad Thickness is equal to or less than zero using the followingcalculation;

Rail SeatAbrasion=ABS(mean(Elev_(E),Elev_(F))−mean(Elev_(A),Elev_(B),Elev_(C))−RailBase Thickness)

Insulator wear, occurring as the insulator pad installed under the toeof the concrete tie fastener clips wears due to traffic loading andlongitudinal rail movements, can be monitored through the measurement ofthe elevation difference between the Top of the Fastener and the Top ofthe Rail Base. The Insulator Thickness can be determined, for example,by using the following calculation;

InsulatorThickness=mean(Elev_(G),Elev_(H))−mean(Elev_(E),Elev_(F))−Fastener ToeThickness

The detailed processing steps for determining pad thickness, rail seatabrasion, and insulator thickness are detailed in the flowchart shown inFIG. 43. The process steps are carried out by a program stored on acomputer readable medium in communication with a processor. A first step(block 520) of the program includes inputting data sets to a processor,such data sets including 3D elevation map data with the data associatedwith the rail heads mathematically removed, rail base edge coordinates,detected tie bounding box data, fastener type and location data, andrail type data. Following 3D Template Matching of all crosstie typespecific 3D models contained in the 3DTAS Feature Library, the bestmatch fastener and anchor features are associated with each crosstiebounding box defined for a given 3D elevation map. Additionalinformation such as location, template match quality, and other physicalparameters are also stored for each feature related to a bounding box.In the case of fastener features, specific measurement points for eachfastener type used for post-processing and analysis are also stored.These measurement points are referenced to the centroid of the specific3D model and are used to determine numerous critical elevations in closeproximity to the fastener which are critical for rail seat abrasion, andinsulator wear calculations. The rail seat abrasion, pad thickness andinsulator thickness measures are determined sequentially, starting withthe first crosstie bounding box to the last tie bounding box containedin the 3D elevation map. The first processing step sets the bounding boxindex to 1 (BBN=1), and identifies all of the fasteners bounded withinthe current crosstie bounding box (BNN) (block 522). Typical crosstiefastening systems have fasteners on the field and gage sides of bothrails, requiring 4 fasteners per crosstie. If the number of fasteners isless than 4 (block 524), the location of missing fastener(s) arerecorded (block 526) and processing continues on the remaining fastenerlocations. For each of the fasteners associated with the crosstiebounding box, the fastener specific reference elevation measurementspoints (such as top of tie, top of rail base, top of fastener, top ofinsulator for example) are extracted (block 528) and a variety ofstatistical elevation measures (mean, median, min, max for example) on apredefined neighborhood centered about each of the elevation referencepoints is calculated (block 530). One Rail Seat Abrasion measure iscalculated using the difference in elevation between the top of railbase and the top of tie minus the Rail Base Thickness parameter (block532). The Insulator Thickness measure is calculated as the difference inelevation between the top of insulator and the top of tie (block 534).These difference measures are calculated for each of the fasteners foundwithin the boundary of the current crosstie bounding box using any ofthe neighborhood based elevation statistics. If the Rail Seat Abrasionmeasure is less than a defined (Rail Seat Abrasion) threshold the RailSeat Flag is set for the appropriate rail within the current boundingbox (block 536). If the Insulator Thickness measure is less than adefined (Insulator Thickness) threshold the Insulator Flag is set forthe appropriate rail within the current bounding box. The process isrepeated for all fasteners associated with each bounding box (block540). Each bounding box is processed sequentially (block 542) until allbounding boxes contained in the 3D elevation map have been analyzed.When all bounding boxes have been analyzed, the Rail Seat Abrasion, PadThickness and Insulator Thickness are associated and stored with thecorresponding bounding box.

Rail anchors 544 are an integral part of crosstie fastening systems asshown in FIG. 42. Installed on the gage side of the base of the railtight to the edge of a crosstie, anchors provide a large bearing surfaceagainst the side of a crosstie to prevent rail creep. The number ofanchors and their position with respect to the track crossties iscrucial for the safe operation of a railway. As part of the 3D analysisand identification of fastening system components, anchors in the 3DTASLibrary are detected and locations reported. Anchors are associated withthe closest proximity crosstie and the anchor type (determined during 3Dmodel matching), location and orientation (skew angle) 546 and crosstieedge offset parameters are calculated (based on accurate longitudinalpositions for the anchor edge and tie bounding box definition) andretained. Anchor and crosstie edge offsets are calculated for both thefield and gage sections of the anchor as shown for example with anchorfield offset 548 and anchor gage offset 550 shown in FIG. 42. Anchorinformation (the presence or absence) is associated and recorded foreach tie. The flow chart for the anchor processing methods is presentedin the 3D Fastener Feature Detection/Parameter Extraction (FIG. 33).

A joint bar is a metal bar that is bolted to the ends of two rails tojoin them together in a track. In continuously welded rail (CWR) jointsand therefore joint bars, can represent repaired locations of interestto rail operators and owners. The 3DTAS exploits the physicaltopographical characteristics of joint bars and their placement toidentify these 3D features. The 3DTAS method for identifying joint barsdetects features in close proximity to the rail head edges which appearat an elevation between the rail base and the top of railhead. Themethod further requires that the joint bars have a longitudinal lengthgreater than a minimum joint bar length threshold and less than amaximum joint bar length threshold. Once detected, the joint baranalysis method verifies the presence of joint bar components on boththe field and gage sides of the rail, identifies any detectable bolt/nutfeatures (e.g., to develop bolt counts or account for missing bolts).

3DTAS surface elevation data for a typical joint bar 600 is shown inFIG. 44. The joint bar processing method also uses the surfaceelevations between the rail head edges to develop a longitudinallyfiltered (smoothed) surface to highlight surface imperfections (railhead corrugation and battered joints). The filtered rail head surface issubtracted from the actual rail head surface elevations to uncoversurface imperfections including open, battered or otherwise damagedjoints as shown in FIG. 45. FIG. 45 shows various features including arail joint 602, a broken rail head 604, joint bars 606, and joint barbolts 608.

The detailed joint bar and rail joint detection and processing steps aredetailed in FIG. 46. The process steps are carried out by a programstored on a computer readable medium in communication with a processor.A first step (block 610) of the program includes inputting data sets toa processor, such data sets including full 3D elevation map data,elevation map sample resolution (longitudinal and transverse), rail headedge coordinates, rail base edge coordinates, and joint bar models. Thedetection of joint bars in the 3DTAS system is based on the detection offeatures of the correct horizontal size and positioned within anacceptable vertical elevation range. The processing steps required tolocate and identify joint bars begins with the definition of 3D jointbar search zones. The 3D joint bar search zone width extends from boththe rail head edge field and gage sides by the Joint Bar Offset amount,and from with a vertical range of above the rail base elevation to belowthe rail head height (block 612). These 3D joint bar search zones areestablished for both rails. Any features with a vertical deviation ofgreater than a defined vertical joint bar deviation threshold occurringat the same longitudinal point in both the field and gage 3D searchzones for either rail are identified as a joint bar targets (block 614).Joint bar targets with dimensions not within the maximum or minimumJoint Bar Model size limits are eliminated as Joint Bar targets (block616). Identified joint bar targets parameters are determined includingphysical dimensions (length, width, and height for example) (block 618).Each of the joint bar targets is analyzed to determine if the physical3D dimension parameters match any of the known Joint Bar Model types(block 620). If the dimensions match a known Joint Bar Model, the typeis determined (block 622), and the proximity to the nearest crosstie(s)(block 624). Following the identification of the Joint Bar type, thejoint bar model includes the bolt pattern definition for both the leftand right rail configurations which includes bolt placement, bolt size,and bolt elevation. The detected joint bar targets are analyzed todetermine the number, orientation and location of bolts using 3Dtemplate matching of threaded bolts, bolt heads, and nut models (block626). Following the identification of joint bars, rail joint analysis ofthe surface of the rail head is completed over the segment of railcorresponding to the limits of each joint bar. The analysis includes thecreation of the rail head surface segment defined by the rail head edgesand the limits of each joint bar. This segment is 2D low pass filtered(with a high pass cutoff of 0.2 m for example) to create the rail headbackground surface. This background is subtracted from the rail headsurface segment to produce the short wavelength anomalies includingcorrugation, separated and battered joints. These parameters areretained for both the left and right rail base surfaces and stored in adata storage apparatus.

Like Rail Seat Abrasion for concrete ties, rail plate damage to woodencrossties through crosstie surface abrasion due to applied loads is asignificant form of distress negatively impacting rail fastener holdingcapabilities and therefore tie condition. Following the identificationof wooden tie fastening components (Spikes and Plate Holes) using 3DTemplate Matching methods, the 3DTAS uses the template correlation mapsfor Spike and Hole targets locations to match the fasteners with thecorrect crosstie Plate model in the 3D Feature Libraries. The detailedwooden crosstie plate detection and processing steps are detailed inFIG. 47. The process steps are carried out by a program stored on acomputer readable medium in communication with a processor. A first step(block 700) of the program includes inputting data sets to a processor,such data sets including 3D elevation map data with the rail headelevation data mathematically removed, elevation map sample resolution(longitudinal and transverse), rail base edge coordinates, tie boundingbox definitions, 3D feature libraries containing the plate models, 3Dhole target maps (showing holes 734 in FIG. 48), and the 3D spike targetmaps (showing spikes 736 in FIG. 48). The detection of wooden crosstieplates in the 3DTAS system is based on the best correlation of thecrosstie plate models in the 3D feature library to the detected spikeand hole patterns. This method reliably detects plates that arepartially cut into the top of the crosstie and no longer have detectableedges around the perimeter of the plate. The first step identifies theleft rail fastener search zone based on limits defined by a lateraloffset (defined by the largest possible plate size) from the rail baseedges (left rail fastener search zone 730 for the left rail and rightrail fastener search zone 732 for the right rail in FIG. 48), andlongitudinal limits defined by the current tie bounding box beinganalyzed (step 702). Once the search limit for the left rail has beendefined, the fastener (Spikes and Hole) target locations within thesearch zone are extracted from the Spike and Hole target maps (step704). Using the fastener location pattern for the search zone defined instep 704, each of the plate models in the 3D feature library are matchedagainst the fastener configuration (step 706) and the correlationstrength of the match is calculated and retained for the best platerotation and offset configuration. After all of the plate models havebeen tested (step 708), the plate model with the highest correlation isretained as the correct left rail plate model (step 710). The process isrepeated for the right rail. The right rail search zone is defined onmaximum plate model lateral offset and current bounding box limits (step712). The right rail fastener target locations are determined (step 714)from the Spike and Hole target maps previously calculated. Once thefastener targets are defined for the search area, each plate modelfastener location is tested for the target locations and the maximumcorrelation is determined, for the optimum rotation and offsetconfiguration (step 716). After all of the plate models are tested forthe right rail fastener targets (step 718), the plate model with themaximum target fastener location correlation (step 720) is retained(plate model 738 in FIG. 48). This process is repeated for all boundingboxes (step 722) and when complete, all of the selected tie plate types,and physical parameters for both rails are associated with thecorresponding tie bounding box and are reported (step 724).

As shown in FIG. 49, following the identification of wooden crosstierail plates, the vertical plate wear (the Plate Cut measure) can becalculated and reported. Following the detection of the correct platetype and orientation, field and gage zones both on the ends of the tieplate (plate field zone 742 for field top of plate, and plate gage zone744 for gage top of plate) and on the surface of the crosstie (surfacefield zone 740 for field top of tie, and surface gage zone 746 for gagetop of tie). The dimensions for the elevation zones are defined by theplate type. The plate cut method also uses the smooth surface mask foreach crosstie region bounded by the tie bounding box to eliminate thoseareas not representative of accurate elevation measures for either therail plate or crosstie surface. Statistical measures for each zone arecalculated (mean, median, max, and min, etc.), and the differencebetween the top of plate and top of tie minus the rail plate thickness(the Plate Cut measure) are recorded for both the field and gage ends ofeach rail plate associated every tie bounding box.

The foregoing description of preferred embodiments of the presentdisclosure has been presented for purposes of illustration anddescription. The described preferred embodiments are not intended to beexhaustive or to limit the scope of the disclosure to the preciseform(s) disclosed. Obvious modifications or variations are possible inlight of the above teachings. The embodiments are chosen and describedin an effort to provide the best illustrations of the principles of thedisclosure and its practical application, and to thereby enable one ofordinary skill in the art to utilize the concepts revealed in thedisclosure in various embodiments and with various modifications as aresuited to the particular use contemplated. All such modifications andvariations are within the scope of the disclosure as determined by theappended claims when interpreted in accordance with the breadth to whichthey are fairly, legally, and equitably entitled.

What is claimed is:
 1. A method of determining pad thickness, rail seatabrasion, and insulator thickness along a railway track bed using asystem for assessing a railway track bed, the method comprising thesteps of: a. inputting elevation data, rail base edge featurecoordinates, detected tie bounding box data, fastener type and locationdata, and rail type data to a processor wherein significant elevationsdue to the rail heads for each rail have been removed from the elevationdata; b. identifying all fasteners in a tie bounding box using theprocessor; c. extracting tie top, rail base, and insulator measurementpoints for each fastener in the tie bounding box using the processor; d.calculating the difference between a rail base elevation and a tie topelevation and adjusting for a rail base thickness to provide a rail seatabrasion measurement sing the processor; e. calculating the differencein elevation between the top of an insulator and the top of the tie toprovide an insulator thickness measurement using the processor; f.flagging the appropriate rail within the tie bounding box if a rail seatabrasion measurement is less than a defined threshold using theprocessor; and g. flagging the appropriate rail within the tie boundingbox if an insulator thickness measurement is less than a definedthreshold using the processor.
 2. The method of claim 1 furthercomprising the steps of determining whether the number of fasteners inthe tie bounding box is less than four and, if the number is less thanfour, recording a broken or missing fastener for that tie bounding boxusing the processor.
 3. The method of claim 1 further comprising thestep of calculating neighborhood elevations for fastener measurementpoints using the processor.
 4. A method of detecting joint bars and railjoints along a railway track bed using a system for assessing a railwaytrack bed, the method comprising the steps of: a. inputting elevationdata, longitudinal and transverse elevation map sample resolution data,rail head edge coordinates, rail base edge coordinates, and joint barmodels to a processor; b. defining a 3D joint bar search zone; c.detecting joint bar targets as objects in the defined 3D joint barsearch zone for the field and gage sides of each rail using theprocessor; d. determining physical parameters for joint bar targetsusing the processor; e. analyzing each joint bar target to determinewhether it matches any known joint bar models using the processor; andf. determining the type of joint bar if a joint bar target matches aknown joint bar model using the processor.
 5. The method of claim 4further comprising the step of eliminating joint bar targets that arenot within the maximum and minimum joint bar model dimensions using theprocessor.
 6. The method of claim 4 further comprising the step ofdetermining the proximity of each joint bar to ties in the elevationdata using the processor.
 7. The method of claim 4 further comprisingthe step of analyzing detected joint bar targets to determine the jointbar bolt configuration using the processor.
 8. The method of claim 4further comprising the step of analyzing rail head surfaces over adefined rail head joint segment surface using the processor.
 9. Themethod of claim 8 further comprising the step of identifying jointseparation, corrugation, and battered joints using the processor.
 10. Amethod of detecting wooden crosstie plates along a railway track bedusing a system for assessing a railway track bed, the method comprisingthe steps of: a. inputting elevation data, longitudinal and transverseelevation map sample resolution data, rail base edge featurecoordinates, detected tie bounding box data, a 3D feature librarycontaining plate models, 3D hole target maps, and 3D spike target mapsto a processor wherein significant elevations due to the rail heads foreach rail have been removed from the elevation data; b. identifying arail fastener search zone using the processor; c. extracting fastenertarget locations from spike and hole target maps using the processor; d.matching plate models from the 3D feature library containing platemodels against the extracted fastener target locations using theprocessor; e. calculating correlation strengths for matches resultingfrom the matching step d. using the processor; and f. retaining theplate model with the highest calculated correlation strength using theprocessor.